Re: st: Incomplete results of linear regression with interaction variable

[John Antonakis <John.Antonakis@unil.ch>]

Hi.

You dont have enough Dfs. See how DFs are calculated for an F-test.

You have k=3 parameters in the numerator and n-k-1 (0) in the denominator.

Did you really mean to estimate a model with n= 4?

Best
J.

__________________________________________

John Antonakis
Professor of Organizational Behavior
Director, Ph.D. Program in Management

Faculty of Business and Economics
University of Lausanne
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Associate Editor
The Leadership Quarterly
__________________________________________

On 20.03.2013 22:56, Jean-Baptiste Peraldi wrote:
> Hi Statalisters,
>
> I want to to run two linear regressions with dichotomous independant 
variables, where one contains an interaction variable.
> It appears that the regression with the interaction variable gives 
only results for the coefficients.
>
> Here is the content of my database:
> ***
> . list
> 
+---------------------------------------------------------------------------+
>      |         race   quality   mean_call    sd_call n         r_q |
> 
|----------------------------------------------------------------------------|
>   1. |           0        0        .0854185       .279624 
1159         0 |
>   2. |           0        1        .1069024       .3091192 
1188         0 |
>   3. |       1        0        .0569456       .2318388 1159         0 |
>   4. |       1        1        .0675791       .2511297 1169         1 |
> 
+---------------------------------------------------------------------------+
> ***
>
>
> The first regression is :
> " mean_call = cst + beta1*race "
> where "race" is a dichotomous (0 or 1) variable.
>
> The second regression contains an interaction variable :
> " mean_call = cst + beta1*race + beta2*quality + beta3*race*quality " 
where both "race" and "quality" are dichotomous (0 or 1) variables.
>
> When running the first regression, I get full results:
> ***
> . reg mean_call race
>
>  Source |      SS                    df       MS            Number of 
obs =       4
> -------------+----------------------------------------- F(  1,     2) 
=    8.00
>   Model      |  .001149076     1  .001149076           Prob > F      
=  0.1056
>   Residual |  .000287314     2  .000143657 R-squared     =  0.8000
> -------------+----------------------------------------- Adj R-squared 
=  0.7000
>        Total |   .00143639         3  .000478797           Root 
MSE      =  .01199
>
> 
------------------------------------------------------------------------------
>    mean_call |      Coef.   Std. Err.      t    P>|t| [95% Conf. 
Interval]
> 
-------------+----------------------------------------------------------------
>         race |   -.033898   .0119857    -2.83   0.106 -.0854683    
.0176723
>        _cons |   .0961604   .0084752    11.35   0.008 .0596947    
.1326261
> 
------------------------------------------------------------------------------
> ***
>
> For the second regression, I create the interaction variable and run 
the regression
> ***
> . gen r_q = race*quality
> . reg mean_call race quality r_q
>
>  Source |         SS                df       MS         Number of obs 
=       4
> -------------+----------------------------------------           F( 
3,     0) =       .
>  Model      |   .00143639     3  .000478797           Prob > F      
=       .
>  Residual |       0                  0           .         
R-squared     =  1.0000
> -------------+----------------------------------------           Adj 
R-squared =       .
>        Total |   .00143639     3  .000478797            Root MSE      
=       0
>
> 
------------------------------------------------------------------------------
>    mean_call |      Coef.   Std. Err.      t    P>|t| [95% Conf. 
Interval]
> 
-------------+----------------------------------------------------------------
>         race |  -.0284728          .        . .            .           .
>      quality |   .0214839          .        . .            .           .
>          r_q |  -.0108504          .        . .            .           .
>        _cons |   .0854185          .        . .            .           .
> 
------------------------------------------------------------------------------
> ***
> Here we can see that we get results for the coefficients only, which 
is quite weird. I will be glad if you can help me solve this problem.
> Thanks for your consideration.
>
> Jean-Baptiste P.
>
> ***
> Stata/IC 12.1 for Mac (64-bit Intel)
> Revision 25 Feb 2013
> ***
>
>
>
>
>
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